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3 cards from this section. Quick, good the night before a test.
What is cross-entropy loss used for?
Think about tasks where categories are involved.
What does MSE stand for and what does it measure?
Consider what happens with predictions that are wrong.
What type of problem does cross-entropy loss primarily address?
Think about what cross-entropy handles in models.
True or False: Mean Squared Error is used in classification problems.
Consider the type of outcomes MSE deals with.
Given the predicted values [3, -0.5, 2, 7] and actual values [2.5, 0.0, 2, 8], calculate the MSE.
Remember to square each difference before summation.
Discuss a scenario where hinge loss would be more beneficial than cross-entropy loss in machine learning.
Consider the types of models you might encounter.
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